Building Intelligence into Financial

Crime Compliance

April | 2020

Organizations are under pressure to improve their financial crime compliance, while at the same time trying to reduce operational costs and improve customer experience. Balancing these demands requires a reconsideration of operating models for antimoney laundering (AML) and know your customer (KYC) compliance, and the re-engineering of supporting IT infrastructure and applications.

An intelligent, driven approach requires organizations to improve their use of data and advanced analytics. This can be supported by artificial intelligence and machine learning. The aim is to reduce false positives and detect suspicious activity early, while managing the cost of operations.

Volume, velocity, veracity and variety of data transactions is driving banks to redesign their data, process, application and technology architectures.

The amount of data required to meet financial crime compliance is increasing at an exponential pace. Current data and analytical infrastructure is unable to cope with this increase.

The accuracy of the data also needs to improve significantly. Data needs to be current at all times. Firms are moving away from periodic reviews of KYC data, and are instead using changes in material data to trigger reviews.

It is also important to dissolve organizational silos. This allows for the sharing of data, customer behavior and intelligence, to further increase the quality of the data and its ability to help detect money laundering and fraud. In the past banks set up operational and technological silos along business units or product processors, resulting in higher costs for financial crimes compliance. Existing technology infrastructure has limited capacity and is unable to support the riskbased approach recommended by regulators.

Figure 1: Breakdown of AML challenges and how they manifest across financial compliance infrastructure.

To combat this, banks are implementing, augmenting and automating customer data collection mechanisms, in the front office and from relationship managers. IT architectures that support AML/KYC are being redesigned to resolve data fragmentation across silos, and common data models are being built, supported by semantic layers. The IT architecture needs to be engineered to support a minimum growth of 25% in processing and storage capacity every year until effective data archival and purging plans are implemented.

Figure 2 illustrates a target architecture to support financial crime compliance. This is an integrated architecture, that sits across all silos and financial crime types—KYC, money laundering, fraud and cyber:

Figure 3: Six key challenge areas faced by financial firms, and the effects of these challenges on monitoring/screening efforts.

Firms are facing challenges across six key areas, leading to high volumes of false positives in transaction monitoring and list screening (Figure 3). Banks have not been able to refine transaction monitoring. As a result, list screening is the predominate method. To compensate, banks have implemented lower thresholds, generating higher volumes of false positives. To address these collective challenges, banks are transforming their AML and KYC target operating models, focusing on four key areas:

Standardizing and consolidating rules and technologies across business areas This includes the creation of global financial crime policies with standardized procedures for KYC, AML and sanctions screening across the globe, that meet the range of regulatory requirements. Firms are also standardizing approaches across channels, eliminating redundant technology platforms and resources. They are leveraging data and other core functions, utilizing robotic processing and hyper-automation to build service models that prevent financial crimes.

Implementing risk-based approaches (RBAs) RBAs use KYC risk ratings to identify high-risk customers and transactions. A risk-based approach must include granular-level customer segmentation, which may use machine learning or other analytical methods such as topological data analysis to establish segments and thresholds, and screen for false positives.

Refreshing transaction monitoring and screening systems on big data platforms The increasing volume of data and the move to risk-based approaches requires firms to revise their transaction monitoring and screening systems for big data platforms. For the best return on this investment, firms should communicate with fraud and cyber to allow a comprehensive view of financial crime data and results. A single platform allows firms to run multiple analytical solutions for financial crimes, providing greater flexibility and access to the proper methodology for any particular problem. Re-implementation of platforms also standardizes customer and product scenarios, reducing redundancies across AML and fraud.

Case management tools also modernize platform performance by providing automatic access to, and accumulation of required data from internal and external sources, and digitizing paper-based documents to support the analysis of the alerts.